Download or read book Efficient Estimation of Dynamic Panel Data Models Under Alternative Sets of Assumptions written by Seung C. Ahn and published by . This book was released on 1993 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Panel Data Econometrics with R written by Yves Croissant and published by John Wiley & Sons. This book was released on 2018-08-10 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Panel Data Econometrics with R provides a tutorial for using R in the field of panel data econometrics. Illustrated throughout with examples in econometrics, political science, agriculture and epidemiology, this book presents classic methodology and applications as well as more advanced topics and recent developments in this field including error component models, spatial panels and dynamic models. They have developed the software programming in R and host replicable material on the book’s accompanying website.

Download or read book Initial conditions and efficient estimation in dynamic panel data models written by Richard Blundell and published by . This book was released on 1991 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Initial Conditions and Efficient Estimation in Dynamic Panel Data Models written by Richard Blundell and published by . This book was released on 1991 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Efficient Estimation of Dynamic Error Components Models with Panel Data written by Lung-Fei Lee and published by . This book was released on 1979 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Student s Solutions Manual and Supplementary Materials for Econometric Analysis of Cross Section and Panel Data second edition written by Jeffrey M. Wooldridge and published by MIT Press. This book was released on 2011-06-24 with total page 391 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the essential companion to the second edition of Jeffrey Wooldridge's widely used graduate econometrics text. The text provides an intuitive but rigorous treatment of two state-of-the-art methods used in contemporary microeconomic research. The numerous end-of-chapter exercises are an important component of the book, encouraging the student to use and extend the analytic methods presented in the book. This manual contains advice for answering selected problems, new examples, and supplementary materials designed by the author, which work together to enhance the benefits of the text. Users of the textbook will find the manual a necessary adjunct to the book.

Download or read book Efficient Estimation with Missing Values in Cross Section and Panel Data written by Bhavna Rai and published by . This book was released on 2021 with total page 139 pages. Available in PDF, EPUB and Kindle. Book excerpt: Chapter 1: Efficient Estimation with Missing Data and EndogeneityI study the problem of missing values in both the outcome and the covariates in linear models with endogenous covariates. I propose an estimator that improves efficiency relative to a Two Stage Least Squares (2SLS) based only on the complete cases. My framework also unifies the literature on missing data and combining data sets, and includes the "Two-Sample 2SLS" as a special case. The method is an extension of Abrevaya and Donald (2017), who provide methods of improving efficiency over complete cases estimators in linear models with cross-section data and missing covariates. I also provide guidance on dealing with missing values in the instruments and in commonly used nonlinear functions of the endogenous covariates, likes squares and interactions, without introducing inconsistency in the estimates.Chapter 2: Imputing Missing Covariate Values in Nonlinear ModelsI study the problem of missing covariate values in nonlinear models with continuous or discrete covariates. In order to use the information in the incomplete cases, I propose an inverse probability weighted one-step imputation estimator that provides gains in efficiency relative to the complete cases estimator using a reduced form for the outcome in terms of the always-observed covariates. Unlike the two-step imputation and dummy variable methods commonly used in empirical work ,my estimator is consistent for a wide class of nonlinear models. It relies only on the commonly used "missing at random" assumption, and provides a specification test for the resulting restrictions. I show how the results apply to nonlinear models for fractional and nonnegative responses.Chapter 3: Efficient Estimation of Linear Panel Data Models with Missing CovariatesWe study the problem of missing covariates in the context of linear, unobserved effects panel data models. In order to use information on incomplete cases, we propose generalized method of moments (GMM) estimation. By using information on the incomplete cases from all time periods, the proposed estimators provide gains in efficiency relative to the fixed effects (and Mundlak) estimator that use only the complete cases. The method is an extension of Abrevaya and Donald(2017), who consider a linear model with cross-sectional data and incorporate the linear imputation method in the set of moment conditions to obtain gains in efficiency. Our first proposed estimator uses the assumption of strict exogeneity of the covariates as well as the selection, while allowing the selection to be correlated with the observed covariates and unobserved heterogeneity in both the outcome equation and the imputation equation. We also consider the case in which the covariates are only sequentially exogenous and propose an estimator based on the method of forward orthogonal deviations introduced by Arellano and Bover (1995). Our framework suggests a simple test for whether selection is correlated with unobserved shocks, both contemporaneous and those in other time periods.

Download or read book Econometric Analysis of Forecasts in Dynamic and Panel Data Models written by Marc A. Mercurio and published by . This book was released on 2000 with total page 458 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Semiparametric Dynamic Panel Data Model written by Subodh Kumar and published by . This book was released on 2000 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Productivity and Efficiency Analysis written by William H. Greene and published by Springer. This book was released on 2015-12-29 with total page 333 pages. Available in PDF, EPUB and Kindle. Book excerpt: This proceedings volume examines the state-of-the art of productivity and efficiency analysis and adds to the existing research by bringing together a selection of the best papers from the 8th North American Productivity Workshop (NAPW). It also aims to analyze world-wide perspectives on challenges that local economies and institutions may face when changes in productivity are observed. The volume comprises of seventeen papers that deal with productivity measurement, productivity growth, dynamics of productivity change, measures of labor productivity, measures of technical efficiency in different sectors, frontier analysis, measures of performance, industry instability and spillover effects. These papers are relevant to academia, but also to public and private sectors in terms of the challenges firms, financial institutions, governments and individuals may face when dealing with economic and education related activities that lead to increase or decrease of productivity. The North American Productivity Workshop brings together academic scholars and practitioners in the field of productivity and efficiency analysis from all over the world. It is a four day conference exploring topics related to productivity, production theory and efficiency measurement in economics, management science, operations research, public administration, and related fields. The papers in this volume also address general topics as health, energy, finance, agriculture, utilities, and economic dev elopment, among others. The editors are comprised of the 2014 local organizers, program committee members, and celebrated guest conference speakers.

Download or read book Panel Data Econometrics written by Mike Tsionas and published by Academic Press. This book was released on 2019-06-19 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: Panel Data Econometrics: Theory introduces econometric modelling. Written by experts from diverse disciplines, the volume uses longitudinal datasets to illuminate applications for a variety of fields, such as banking, financial markets, tourism and transportation, auctions, and experimental economics. Contributors emphasize techniques and applications, and they accompany their explanations with case studies, empirical exercises and supplementary code in R. They also address panel data analysis in the context of productivity and efficiency analysis, where some of the most interesting applications and advancements have recently been made. Provides a vast array of empirical applications useful to practitioners from different application environments Accompanied by extensive case studies and empirical exercises Includes empirical chapters accompanied by supplementary code in R, helping researchers replicate findings Represents an accessible resource for diverse industries, including health, transportation, tourism, economic growth, and banking, where researchers are not always econometrics experts

Download or read book The Oxford Handbook of Panel Data written by Badi Hani Baltagi and published by . This book was released on 2015 with total page 705 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Oxford Handbook of Panel Data examines new developments in the theory and applications of panel data. It includes basic topics like non-stationary panels, co-integration in panels, multifactor panel models, panel unit roots, measurement error in panels, incidental parameters and dynamic panels, spatial panels, nonparametric panel data, random coefficients, treatment effects, sample selection, count panel data, limited dependent variable panel models, unbalanced panel models with interactive effects and influential observations in panel data. Contributors to the Handbook explore applications of panel data to a wide range of topics in economics, including health, labor, marketing, trade, productivity, and macro applications in panels. This Handbook is an informative and comprehensive guide for both those who are relatively new to the field and for those wishing to extend their knowledge to the frontier. It is a trusted and definitive source on panel data, having been edited by Professor Badi Baltagi-widely recognized as one of the foremost econometricians in the area of panel data econometrics. Professor Baltagi has successfully recruited an all-star cast of experts for each of the well-chosen topics in the Handbook.

Download or read book Journal of Econometrics written by and published by . This book was released on 1995 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Large dimensional Panel Data Econometrics Testing Estimation And Structural Changes written by Feng Qu and published by World Scientific. This book was released on 2020-08-24 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims to fill the gap between panel data econometrics textbooks, and the latest development on 'big data', especially large-dimensional panel data econometrics. It introduces important research questions in large panels, including testing for cross-sectional dependence, estimation of factor-augmented panel data models, structural breaks in panels and group patterns in panels. To tackle these high dimensional issues, some techniques used in Machine Learning approaches are also illustrated. Moreover, the Monte Carlo experiments, and empirical examples are also utilised to show how to implement these new inference methods. Large-Dimensional Panel Data Econometrics: Testing, Estimation and Structural Changes also introduces new research questions and results in recent literature in this field.

Download or read book Estimation of Panel Data Models with Missing Covariate Values written by Jessie Elizabeth Coe and published by . This book was released on 2019 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation presents methods for (chapters 1 and 3,) and empirical applications of (chapters 2 and 3,) estimation of panel data models in the presence of missing covariate values. Chapter 1 considers estimation of a linear fixed effects model in which covariate values may be missing. Two inverse probability weighted (IPW) estimators are proposed. The main assumption is a missing at random assumption (MAR) which allows missingness (observation) to be related to the outcome and its shocks, but requires that the probability of observation is not related to the missing values. The inverse of the estimated probability of observation is used to re-weight the estimating equations, which are then estimated in a second stage by either computationally simple pooled OLS, or more asymptotically efficient GMM. Both of the proposed estimators are consistent and [square root] N-asymptotically normal, and the asymptotic variance is derived. The main results are developed for the classical linear fixed effects model under strict exogeneity, and the approach generalizes to many panel models, including dynamic linear unobserved effects models. Chapter 2 revisits the question of the impact of local water quality in local water amenities on housing values, as in (22). Water quality, the main covariate of interest, as measured by the level of dissolved oxygen, is missing for many properties in many time periods. This chapter investigates the sensitivity of estimates of the value of local water quality to the treatment of the missing data. The inverse probability weighted estimator of chapter 1 is compared to the unweighted estimator used in (22). Empirical evidence suggests that the MAR assumption is more palatable than the assumption necessary for the more commonly used unweighted estimator. The estimation results change in both magnitude and statistical significance when the IPW estimator is used. The third chapter considers estimation of a linear fixed effects model under an ignorable missingness assumption, which assumes that observation of the covariates is not directly related to the outcome or the unobserved errors, and includes missing completely at random as a special case. Under this assumption, using the complete data will consistently estimate the coefficients, but may result in a loss of efficiency from the decreased sample size used in estimation. I propose a generalized method of moments (GMM) estimator that uses all the data, is not difficult to implement, and yields potential efficiency gains over the complete data method. For the classical linear fixed effects model with homoskedasticity, efficiency gains are realized in almost all cases. The estimator imputes a value for the missing covariates by including an additional moment in the estimation, and thus accurately accounts for the uncertainty in imputation, unlike common single imputation methods, and does not require a distributional assumption, unlike multiple imputation methods. The assumption required is that the linear projection of the missing covariates onto the fully observed variables is the same for the observed values and the missing values of the covariates. Simulation results show efficiency gains in finite samples, and an empirical illustration based on (3)'s analysis of the effect of life expectancy on economic growth is explored using both the complete data method, and the proposed GMM estimator

Download or read book Index Number and Factor Demand Approaches to the Estimation of Productivity written by David H. Good and published by . This book was released on 1996 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Maximum Likelihood and GMM Estimation of Dynamic Panel Data Models with Fixed Effects written by Hugo Kruiniger and published by . This book was released on 2002 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper considers inference procedures for two types of dynamic linear panel data models with fixed effects (FE). First, it shows that the closures of stationary ARMAFE models can be consistently estimated by Conditional Maximum Likelihood Estimators and it derives their asymptotic distributions. Then it presents an asymptotically equivalent Minimum Distance Estimator which permits an analytic comparison between the CMLE for the ARFE (1) model and the GMM estimators that have been considered in the literature. The CMLE is shown to be asymptotically less efficient than the most efficient GMM estimator when N approaches the limit infinity but T is fixed. Under normality some of the moment conditions become asymptotically redundant and the CMLE attains the Cramer-Rao lowerbound when T approaches the limit infinity as well. The paper also presents likelihood based unit root tests. Finally, the properties of CML, GMM, and Modified ML estimators for dynamic panel data models that condition on the initial observations are studied and compared. It is shown that for finite T the MMLE is less efficient than the most efficient GMM estimator.